Latest revision as of 14:54, 1 November 2018

Thesis Outline

Thesis Statement

Topic

Ways of understanding complex algorithms. Ways of understanding machine learning algorithms. trough reverse engineering.

Reverse Engineering Algorithmic Images

Visual Interventions

Focus

in times of machine learning

Argument

Machine Learning is intervening in our visual world and therefore shaping how we perceive.

Revise

Machine Learning is intervening in our visual world and therefore shaping how we perceive. Algorithms analyse images, process data and even generate new images, while these algorithms are black boxes.

Scope

»Intelligent« algorithms are everywhere and they challenge the way we see. They generate new kinds of images that raise a new meaning and understanding of the world. How can we understand these algorithms in order to understand their influence on our visual perception.

Outline

Introduction

Background

I'm specifically interested in algorithms and their influence on visual culture
Machine learning algorithms are everywhere and increasingly intervene in our world and how we perceive it

Body

Machine Learning Interventions in visual culture

Examination of current applications of machine learning algorithms
- Influence on visual culture
- short history of algorithmic image production / analysis
Problematic of machine learning algorithms and models

Examining Machine Learning

What machine learning is, where it is and what it does

Computer Vision

Algorithms that see and interpret our world
How algorithms intervene in our world
Visibility for Computers and Humans:
- How we help computers to help them see
- Therefore: how computers help us see
- Computer challenge what and how we see
- Machines see things that remain invisible for humans
- Relation to the invisibility of models in machine learning algorithms ->

Computer Visual Generation

Algorithms that generate visual material -> images

Image Language (A bag of features)

Images as analytical tools
Features of images
How we talk about images in relation to how they are generated

Understanding Machine Learning: Reverse Engineering

Experimental approach
Black Box and how to approach this complexity

Towards endless image production

The computer allows to generate every imaginable image (imagination = image)
From mechanical reproduction to mechanical production

Conclusion

How understanding and approaches like reverse engineering help influencing complex systems.

@@ Line 3: / Line 3: @@
 ===Topic===
 Ways of understanding complex algorithms.
-machine learning
+Ways of understanding machine learning algorithms.
-reverse engineering
+trough reverse engineering.
-Reverse Engineering Neural Networks
 Reverse Engineering Algorithmic Images
-Visual Forms of machine learning
+Visual Interventions
 ===Focus===
 in times of machine learning
 ===Argument===
-New advanced algorithms allow for new form understanding and generation.
+Machine Learning is intervening in our visual world and therefore shaping how we perceive.
 ===Revise===
-New advanced algorithms allow for new form understanding and generation. This means a new understanding of image perception and production in the arts and design.
+Machine Learning is intervening in our visual world and therefore shaping how we perceive. Algorithms analyse images, process data and even generate new images, while these algorithms are black boxes.
-(Machine learning algorithms can be used to generate new photorealistic images. These images enable a new kind of aesthetic and allow for a new understanding of these technologies at the same time. )
 ===Scope===
-»Intelligent« algorithms are everywhere and they challenge the way we see. They generate new kinds of images that raise a new meaning and understanding of the world. What do these images mean for cultural production like art and design?
+»Intelligent« algorithms are everywhere and they challenge the way we see. They generate new kinds of images that raise a new meaning and understanding of the world. How can we understand these algorithms in order to understand their influence on our visual perception.
-==Outline==
-===Introduction===
-====Background====
-====Thesis Statement====
-===Body===
-====Database (History of image generation in art and design)====
-* Tracing back the history of algorithmic image generation and how it changed due to new technologies
-** Instructions as Art (LeWitt, Gerstner)
-** Draw analogies to the invention of the camera (Walter Benjamin: Essay on Photography)
-** also Cinema and introduction of databases (Lev Manovich: The database as symbolic form)
-***database-art (The aesthetic of the database)
-====Model (From Database to Model)====
+=Outline=
-in realation to images
+==Introduction==
-* Instructions/Algorithm become invisible
+===Background===
-* Images are generated on basis of a database
+* I'm specifically interested in algorithms and their influence on visual culture
+* Machine learning algorithms are everywhere and increasingly intervene in our world and how we perceive it
+==Body==
+===Machine Learning Interventions in visual culture===
+* Examination of current applications of machine learning algorithms
+** Influence on visual culture
+** short history of algorithmic image production / analysis
+* Problematic of machine learning algorithms and models
-====Image Language====
+===Examining Machine Learning===
-=====On the visible and invisible=====
+* What machine learning is, where it is and what it does
-* Computer vision
+====Computer Vision====
+* Algorithms that see and interpret our world
+* How algorithms intervene in our world
+* Visibility for Computers and Humans:
 ** How we help computers to help them see
 ** Therefore: how computers help us see
-*** Computer challenge what and how we see
+** Computer challenge what and how we see
-*** Machines see things that remain invisible for humans
+** Machines see things that remain invisible for humans
-**** Relatable to images or models or databases or technology and lack of understanding
+** Relation to the invisibility of models in machine learning algorithms ->
+====Computer Visual Generation====
+* Algorithms that generate visual material -> images
-=====Understanding Images=====
+====Image Language (A bag of features)====
 * Images as analytical tools
-* Features of images and how we talk about images
+* Features of images
+* How we talk about images in relation to how they are generated
+===Understanding Machine Learning: Reverse Engineering===
+* Experimental approach
+* Black Box and how to approach this complexity
 ====Towards endless image production====
 * The computer allows to generate every imaginable image (imagination = image)
 * From mechanical reproduction to mechanical production
-(Do not know where to put this questions:)
-* machine to machine, why would they need vision?
-* why do we need images?
 ===Conclusion===
+How understanding and approaches like reverse engineering help influencing complex systems.
-??
-Images change the way we perceive the world and therefore we need to be able to understand the way images are being created.
-Turn it around!
-From specific problem > more broader terms

User:Alexander Roidl/thesis-outline2: Difference between revisions